{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:27:00Z","timestamp":1773188820016,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2023YFC3206202"],"award-info":[{"award-number":["2023YFC3206202"]}]},{"name":"National Key R &amp; D Program of China","award":["2023YFC3209102"],"award-info":[{"award-number":["2023YFC3209102"]}]},{"name":"National Key R &amp; D Program of China","award":["SKS-2022008"],"award-info":[{"award-number":["SKS-2022008"]}]},{"name":"Major Science and Technology Projects","award":["2023YFC3206202"],"award-info":[{"award-number":["2023YFC3206202"]}]},{"name":"Major Science and Technology Projects","award":["2023YFC3209102"],"award-info":[{"award-number":["2023YFC3209102"]}]},{"name":"Major Science and Technology Projects","award":["SKS-2022008"],"award-info":[{"award-number":["SKS-2022008"]}]},{"name":"Department of Environment and Society, Quinney College of Natural Resources, Utah State University","award":["2023YFC3206202"],"award-info":[{"award-number":["2023YFC3206202"]}]},{"name":"Department of Environment and Society, Quinney College of Natural Resources, Utah State University","award":["2023YFC3209102"],"award-info":[{"award-number":["2023YFC3209102"]}]},{"name":"Department of Environment and Society, Quinney College of Natural Resources, Utah State University","award":["SKS-2022008"],"award-info":[{"award-number":["SKS-2022008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou County, Sichuan Province, as a case study, we developed an evaluation index system incorporating 14 factors. We employed three base models\u2014logistic regression, support vector machine, and Gaussian Naive Bayes\u2014assessed through four ensemble methods: Stacking, Voting, Bagging, and Boosting. The decision mechanisms of these models were explained via a SHAP (SHapley Additive exPlanations) analysis. Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. This approach effectively addresses the challenges of unreliable landslide susceptibility mapping in complex environments.<\/jats:p>","DOI":"10.3390\/rs16224218","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T12:49:56Z","timestamp":1731415796000},"page":"4218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Landslide Susceptibility Mapping Based on Ensemble Learning in the Jiuzhaigou Region, Sichuan, China"],"prefix":"10.3390","volume":"16","author":[{"given":"Bangsheng","family":"An","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIRCAS), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Environment and Society, Quinney College of Natural Resources, Utah State University, Logan, UT 84322, USA"}]},{"given":"Shenqing","family":"Xiong","sequence":"additional","affiliation":[{"name":"China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2607-4628","authenticated-orcid":false,"given":"Wanchang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIRCAS), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yaning","family":"Yi","sequence":"additional","affiliation":[{"name":"National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China"}]},{"given":"Zhixin","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIRCAS), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Chuanqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute (AIRCAS), Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1080\/10106049.2018.1499820","article-title":"A Novel Hybrid Approach of Bayesian Logistic Regression and Its Ensembles for Landslide Susceptibility Assessment","volume":"34","author":"Abedini","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-Based Landslide Susceptibility Models Using Frequency Ratio, Logistic Regression, and Artificial Neural Network in a Tertiary Region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104451","DOI":"10.1016\/j.catena.2019.104451","article-title":"A Spatially Explicit Deep Learning Neural Network Model for the Prediction of Landslide Susceptibility","volume":"188","author":"Dong","year":"2020","journal-title":"Catena"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105344","DOI":"10.1016\/j.catena.2021.105344","article-title":"GIS-Based Comparative Study of Bayes Network, Hoeffding Tree and Logistic Model Tree for Landslide Susceptibility Modeling","volume":"203","author":"Chen","year":"2021","journal-title":"Catena"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101626","DOI":"10.1016\/j.asej.2021.10.021","article-title":"Landslide Hazard Assessment Using Analytic Hierarchy Process (AHP): A Case Study of National Highway 5 in India","volume":"13","author":"Panchal","year":"2022","journal-title":"Ain Shams Eng. 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